Preoperative Statin Use and Infection after Cardiac Surgery: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: It has been suggested that the routine use of statins preoperatively would reduce the risk of postoperative infection. We conducted this study to explore whether preoperative statin use was associated with infection after cardiac surgery (recipients of which have a higher-than-average risk of postoperative infection). METHODS: We performed secondary analysis of data collected in a prospective cohort study of adults who underwent nontransplant cardiac surgery in a university hospital during the period January 1999 through December 2005. Outcomes were ascertained in a blinded and independent fashion. RESULTS: Of the 7733 patients, 2657 (34%) were taking statins preoperatively; the proportion increased from 16% during 1999-2000 to 53% during 2003-2005 (P < .001, by test for trend). There was no association between preoperative statin use and postoperative infection: 214 statin users (8.1%) versus 425 statin nonusers (8.4%) developed an infection within 30 days after surgery. Factors associated with increased risk of infection after cardiac surgery included diabetes mellitus, heart failure, chronic obstructive pulmonary disease, increasing age, elevated baseline creatinine level, and longer duration of cardiopulmonary bypass but not statin use (adjusted odds ratio, 1.08; 95% confidence interval, 0.89-1.31). CONCLUSIONS: Preoperative statin use was not associated with a reduction in the rate of postoperative infection among patients who underwent cardiac surgery. This lack of apparent benefit for high-risk patients argues against the routine use of statins as a preoperative strategy for lower-risk patients and supports calls for randomized trials to define whether preoperative statin use influences postoperative rates of infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".